A Novel Approach of Area-Efficient FIR Filter Design Using Distributed Arithmetic with Decomposed L

IOSR Journal of Electronics and Communication Engineering (IOSR-JECE)

e-ISSN: 2278-2834,p- ISSN: 2278-8735. Volume 7, Issue 2 (Jul. - Aug. 2013), PP 13-18 www.iosrjournals.org

www.iosrjournals.org 13 | Page

A Novel Approach of Area-Efficient FIR Filter Design Using

Distributed Arithmetic with Decomposed LUT

1P.Sravanthi,

2CH.Srinivasa Rao,

3S.Madhava Rao,

1M.TECH student, MLE College of Engineering, S.Konda, prakasam(DT), A.P, 2,3ASSOC.Professor, MLECollege, singarayakonda, prakasam, A.P, INDIA,

Abstract: In this paper, a highly area-efficient multiplier-less FIR filter is presented. Distributed Arithmetic

(DA) has been used to implement a bit-serial scheme of a general asymmetric version of an FIR filter, taking

optimal advantage of the 3-input LUT-based structure of FPGAs. The implementation of FIR filters on FPGA

based on traditional arithmetic method costs considerable hardware resources, which goes against the decrease

of circuit scale and the increase of system speed. This paper presents the realization of area efficient

architectures using Distributed Arithmetic (DA) for implementation of Finite Impulse Response (FIR) filter. The

performance of the bit-serial and bit parallel DA along with pipelining architecture with different quantized

versions are analyzed for FIR filter Design. Distributed Arithmetic structure is used to increase the resource

usage while pipeline structure is also used to increase the system speed. In addition, the divided LUT method is

also used to decrease the required memory units. However, according to Distributed Arithmetic, we can make a

Look-Up-Table (LUT) to conserve the MAC values and callout the values according to the input data if necessary. Therefore, LUT can be created to take the place of MAC units so as to save the hardware resources.

The simulation results indicate that FIR filters using Distributed Arithmetic can work stable with high speed and

can save almost 50 percent hardware resources to decrease the circuit scale, and can be applied to a variety of

areas for its great flexibility and high reliability. This method not only reduces the LUT size, but also modifies

the structure of the filter to achieve high speed performance.

Keywords: DSP, Digital Filters, FIR , FPGA, MAC, Distributed Arithmetic(DA),Divided LUT, pipeline

I. Introduction In the recent years, there has been a growing trend to implement digital signal processing functions in

Field Programmable Gate Array (FPGA). In this sense, we need to put great effort in designing efficient architectures for digital signal processing functions such as FIR filters, which are widely used in video and

audio signal processing, telecommunications and etc.

Traditionally, direct implementation of a K-tap FIR filter requires K multiply-and-accumulate (MAC)

blocks, which are expensive to implement in FPGA due to logic complexity and resource usage. To resolve this

issue, we first present DA, which is a multiplier-less architecture.

Implementing multipliers using the logic fabric of the FPGA is costly due to logic complexity and area

usage, especially when the filter size is large. Modern FPGAs have dedicated DSP blocks that alleviate this

problem, however for very large filter sizes the challenge of reducing area and complexity still remains. An

alternative to computing the multiplication is to decompose the MAC operations into a series of lookup table

(LUT) accesses and summations. This approach is termed distributed arithmetic (DA), a bit serial method of

computing the inner product of two vectors with a fixed number of cycles. The original DA architecture stores all the possible binary combinations of the coefficients w[k] of

equation (1) in a memory or lookup table[5,7,9]. It is evident that for large values of L, the size of the memory

containing the pre computed terms grows exponentially too large to be practical. The memory size can be

reduced by dividing the single large memory (2Lwords) into m multiple smaller sized memories each of size 2k

where L = m × k. The memory size can be further reduced to 2L−1 and 2L−2 by applying offset binary

coding and exploiting resultant symmetries found in the contents of the memories[8,9].

This technique is based on using 2's complement binary representation of data, and the data can be pre-

computed and stored in LUT. As DA is a very efficient solution especially suited for LUT-based FPGA

architectures, many researchers put great effort in using DA to implement FIR filters in FPGA. Patrick Longa

introduced the structure of the FIR filter using DA algorithm and the functions of each part. Sangyun Hwang

analyzed the power consumption of the filter using DA algorithm[3,7]. Heejong Yoo proposed a modified DA

architecture that gradually replaces LUT requirements with multiplexer/adder pairs. But the main problem of DA is that the requirement of LUT capacity increases exponentially with the order of the filter, given that DA

implementations need 2Kwords (K is the number of taps of the filter)[4,5,10]. And if K is a prime, the hardware

resource consumption will cost even higher. To overcome these problems, this paper presents a hardware-

efficient DA architecture.

A Novel Approch Of Area-Efficient Fir Filter Design Using Distributed Arithmetic With Decomposed


This method not only reduces the LUT size, but also modifies the structure of the filter to achieve high

speed performance. The proposed filter has been designed and synthesized with ISE 7.1, and implemented with

a 4VLX40FF668 FPGA device. [2] Our results show that the proposed DA architecture can implement

FIR filters with high speed and smaller resource usage in comparison to the previous DA architecture.

II. Design Methodology 2.1 Distributed Arithmetic FIR filter architecture

Distributed Arithmetic is one of the most well-known methods of implementing FIR filters. The DA

solves the computation of the inner product equation when the coefficients are pre knowledge, as happens in

FIR filters.

An FIR filter of length K is described as:

1

0

][][][K

k

knxkhny --(1)

Where h[k] is the filter coefficient and x[k] is the input data. For the convenience of analysis, x'[k] =x [n - k] is used for modifying the equation (1) and we have:

1

0

' ][].[K

k

kxkhy ........(2)

Then we use B-bit two's complement binary numbers to represent the input data:

1

0

' 2][][2][B

b

b

bB

B kxkxkx -(3)

Where ][kxb denotes the b’th bit of x[k], ][kxb {0, 1}.Substitution of (3) into (2) yields:

1

0

1

0

)2][][2(][K

k

B

b

b

bB

B kxkxkhy

1

0

1

0

1

0

][][2][][2K

k

B

b

K

k

b

b

B

B kxkhkxkh

])[],[(2])[],[(21

0

kxkhfkxkhf b

B

b

b

B

B

- (4)

We have

][][])[],[(1

0

kxkhkxkhf b

K

k

b

In equation (4), we observe that the filter coefficients can be pre-stored in LUT, and addressed by xb =

[ ]0[bx , ]1[bx ,... ]1[ Kxb ].This way, the MAC blocks of FIR filters are reduced to access and summation

with LUT.

The implementation of digital filters using this arithmetic is done by using registers, memory resources

and a scaling accumulator.

Original LUT-based DA implementation of a 4-tap (K=4) FIR filter is shown in Figure 2.1. The DA

architecture includes three units: the shift register unit, the DA-LUT unit, and the adder/shifter unit.

Figure 2.1: Original LUT-based DA implementation of a 4-tap filter



2.2 Distributed Arithmetic Architecture With Out Lut:

In Fig. 2.1, we can see that the lower half of LUT (locations where 3b =1) is the same with the sum of

the upper half of LUT (locations where 3b =0) and h [3]. Hence, LUT size can be reduced 1/2 with an additional

2x1 multiplexer and a full adder, as shown in Figure 2.2.2(a). By the same LUT reduction procedure, we can have the final LUT-less DA architectures, as shown in

Figure 2.2.2(b). On other side, for the use of combination logic circuit, the filter performance will be affected.

But when the taps of the filter is a prime, we can use 4-input LUT units with additional multiplexers and full

adders to get the trade off between filter performance and small resource usage.

Figure 2.2.2(a): Proposed DA architecture for a 4-tap filter (32 word LUT implementation of DA)

Figure 2.2.2(b): LUT-less DA architectures

III. Distributed Arithmetic With 4-Input Look Up Table Architecture

The main advantage of FIR filters is their linear-phase response. It is only achieved with symmetrical

or ant symmetrical structures which, furthermore, offer hardware reductions.

As the filter order K increases, the LUT size (K2 words) grows exponentially. With this it is difficult

to implement the architecture using lut less method as more and more combinational logic is needed. This

resulted in a new architecture which is explained below.

3.1 Four input look up table architecture:

As the filter order K increases, the LUT size (K2 words) grows exponentially. We can divide the K-

tap FIR filter into L groups of smaller filters (K = L *M). Hence the LUT size is reduced to ML 2 words,

and then we have:

1

0

1

0

1

0

][][][][LM

k

L

l

M

m

mlMxmlMhkxkhy



We would better set M=4, because only under this condition can we take optimal advantage based on

the 4-input LUT structure of FPGA, as shown in Fig 3.1. If we can not obtain M=4 after the calculation, we can

use both the proposed DA-LUT unit and the original 4-input DA-LUT unit to achieve our goal.

Xb[0] …. .x1[0] x0[0]

LUT

LUT

LUT

LUT

ACC+/-

+

+

+

Xb[M-1] .. .x1[M-1] x0[M-1]

Xb[M] …. x1[M] x0[M]

Xb[2M-1] … x1[2M-1] x0[2M-1]

Xb[2M] … x1[2M] x0[2M]

Xb[3M-1] .... x1[3M-1] x0[3M-1]

Xb[3M] .. x1[3M] x0[3M]

Xb[4M-1] .. x1[4M-1] x0[4M-1]

t2

Figure 3.1 DISTRIBUTED ARITHMETIC WITH 4-INPUT LOOK UP TABLE

IV. Simulation Results: Matlab and Modelsim are used as the simulation platforms. We can analysis the changes between the

input wave and the output wave to observe the permanence of the designed filter through Matlab , while

observing the real-time implementation performance of FPGA through Modelsim.

To observe the performance of the designed filter, a square signal of 1.6MHz is generated as the input

by Matlab, and the sampling frequency is 32MHz. The data is changed into the two's complement form and

stored in the file named fir_in.txt. And the data is called out later as the input of the FPGA filter through

testbench language in Modelsim, the output data is shown in the waveform workstation and also stored into the

file, which can be read by Matlab to make a contrast to analysis.

Original LUT-based DA implementation of a

4-tap filter

4-tap LUT-less DA architectures for a 4-tap FIR filter



4-input look up table architecture for high order filters Without pipelining

4-input look up table architecture with pipelining

Fully parallel DA architecture FIR filter without pipelining

Fully parallel DA architecture FIR filter with pipelining

V. Conclusion And Future Scope This paper presents the proposed DA architectures for high-order filters. The architectures reduce the

memory usage by half at every iteration of LUT reduction at the cost of the limited decrease of the system

frequency. We also divide the high-order filters into several groups of small filters; hence we can reduce the

LUT size also. As to get the high speed implementation of FIR filters, a full-parallel version of the DA

architecture is adopted.

We have successfully implemented a high-efficient 70-tap full-parallel DA filter, using both an original

DA architecture and a modified DA architecture on a 4VLX40FF668 FPGA device. It shows that the proposed

DA architectures are hardware efficient for FPGA implementation. The limitation of this project is that by the

use of pipelining speed is increased however, the resource usage increase more than B times of the bit-serial

one's, since we need to use pipeline registers and additional adders. Also the number of LUT’s required would be enormous. The future scope of this project is to improve the architecture of the Distributed arithmetic FIR

filter such that it uses the hardware resources of the latest FPGA families. In vertex-5 and Vertex-6 family

FPGA’s, 6-input LUT’s were introduced. Future work includes changing the architecture which uses 6-input



LUT’s for storing coefficient sums and SRL(Shift register logic) macros to implement shift operations such that

total number of slices used will be reduced.

References [1]. Partrick Longa, Ali Miri, "Area-Efficient Fir Filter Design on FPGAs using Distributed Arithmetic" IEEE International Symposium

on Signal Processing and Information Technology, pp:248-252,2006

[2]. Sangyun Hwang, Gunhee Han,Sungho Kang, Jaeseok Kim, "New Distributed Arithmetic Algorithm for Low-Power FIR Filter

Implementation", IEEE Signal Processing Letters, Vol.11, No5, pp:463-466,May, 2004.

[3]. Heejong Yoo, David V.Anderson, "Hardware-Efficient Distributed Arithmetic Architecture For High-order Digital Filters", IEEE

International Conference on Acoustics, Speech and Signal Processing, Vol.5,pp: 125-128,March,2005

[4]. Wangdian, Xingwang Zhuo "Digital Systems Applications and Design Based On Verilog HDL", Beijin: National Defence Industry

press, 2006.

[5]. McClellan , J.H. Parks, T.W. Rabiner, L.R. "A computer program for designing optimum FIR linear phase digital filters":. IEEE

Trans. Audio Electroacoust. Vol. 21, No.6, pp:506-526, 1973. http://en.wikipedia.org/wiki/instruction_pipeline

[6]. Httl://cse.stanford.edu/class/sophomore-college/projects-00/risc/pipelining/index.html we Meyer-Baese.Digital signal processing

with FPGA[M].Beijing:Tsinghua University Press,2006:50~51

[7]. Tsao Y C and Choi K. Area-Efficient Parallel FIR Digital Filter Structures for Symmetric Convolutions Based on Fast FIR

Algorithm [J]. IEEE Transactions on Very Large Scale Integration (VLSI) Systems, 2010,PP(99):1~5.

[8]. Chao Cheng and Keshab K Parhi. Low-Cost Parallel FIR Filter Structures With 2-Stage Parallelism[J].IEEE Transactions on

Circuits and Systems I:Regular,2007,54(2):280~290.

[9]. Tearney G J and Bouma B E. Real-Time FPGA Processing for High-Speed Optical Frequency Domain Imaging [J].IEEE

Transactions on Medical Imaging, 2009,28(9):1468~1472.

http://en.wikipedia.org/wiki/instruction_pipeline

http://en.wikipedia.org/wiki/instruction_pipeline

A Novel Approach of Area-Efficient FIR Filter Design Using Distributed Arithmetic with Decomposed L

Documents

efficient fir filter

distributed arithmetic

implementation of fir

arithmetic structure

filter size

lut size

distributed arithmeticda

decomposed lut